The invention is generally directed to providing online services. In particular it provides a method and apparatus suitable for providing a cookie to a customer and providing online services based on that cookie.
Product recommendation engines provide personalized recommendations as part of an electronic commerce (eCommerce) solution to improve sales revenue and product attachment for clients. These engines operate in an environment outside of the eCommerce trading engine, which means that data is commonly collected using a system of website tagging, with access to transactional data not something readily accessible. However, with a proliferation of multiple browsers and devices used by consumers it is progressively difficult to provide accurate recommendations using all available data using such a transactional third party analytical approach without access to individual customer data from master data sources.
Currently in most recommendation frameworks, the historical view, cart, and purchase activity of an individual user on a specific machine/device is stored in a local cookie on that user's same machine with a cookie identification (id) unique to that machine/device. Cookies are commonly used to help personalize recommendations served on retail websites by using information based on what the user last viewed, carted, or purchased. However, without correlating together the browsing history across browsers operating on multiple devices and browsers the recommendation engine has an incomplete picture of recent history, meaning that the most accurate recommendations cannot be served. Cookie data is also stored locally for performance reasons, for example, so that the server need not be repeatedly queried for the same information.
Prior art solutions provide the synchronization of cookies, but with a user opting into devices, with the synchronization provided by a cookie exchange server. Other solutions require browser plugins to provide the necessary aggregation.